Sampling multisensory information and taking the appropriate motor action is critical for a biological organism’s survival, but a difficult task for robots. We present a Neurally...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Abstract-- In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different obje...
Abstract. We consider the case where inconsistencies are present between a system and its corresponding model, used for automatic verification. Such inconsistencies can be the resu...
Off-line trained class-specific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, o...